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Data Augmentation Techniques for Chinese Disease Name Normalization

Data Augmentation Techniques for Chinese Disease Name Normal...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Cui, Wenqian Fu, Xiangling Liu, Shaohui Gu, Mingjun Liu, Xien Wu, Ji King, Irwin The Chinese University of Hong Kong Dept. Computer Science and Enginnering Hong Kong Beijing China Tsinghua University Dept. Electronic Engineering Beijing China Ai Tsinghua University Dept. Electronic Engineering College Beijing China

ISBN: (纸本)9798350386226

Disease name normalization is an important task in the medical domain. It classifies disease names written in various formats into standardized names, serving as a fundamental component in smart healthcare systems for various disease-related functions. Nevertheless, the most significant obstacle to existing disease name normalization systems is the severe shortage of training data. Consequently, we present a novel data augmentation approach that includes a series of data augmentation techniques and some supporting modules to help mitigate the problem. Through extensive experimentation, we illustrate that our proposed approach exhibits significant performance improvements across various baseline models and training objectives, particularly in scenarios with limited training data © 2024 IEEE.

关键词： Natural language processing systems

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Surveillance Algorithm for a Swarm of Self-Organized Aerial Unmanned Vehicle 2

Surveillance Algorithm for a Swarm of Self-Organized Aerial ...

引用

2nd International Conference for Advancement in Technology, ICONAT 2023

作者： Shivkumar, S. Nippun Kumaar, A.A. Amrita School of Engineering Dept. of Electronics and Communication & Engineering Bengaluru Amrita Vishwa Vidyapeetham India Amrita School of Computing Dept. of Computer Science & Engineering Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (纸本)9781665475174

Unmanned aerial vehicles (UAVs) are currently being used in various domains and are the current big revolution. The use of autonomous UAVs is even promising for providing new opportunities. Various applications such as surveying, monitoring, search & rescue operations, and delivery would be beneficial if it is done autonomously without human interference. The performance can also be enhanced if a swarm of autonomous UAVs is used. For each and every UAV to contribute effectively and to reduce the task level complexity of individual UAVs the concept of self-organization could be incorporated into the swarm. Self-organization is an individual UAV performing a simple task without any knowledge of the overall goal but indirectly helping to achieve the overall goal. In this paper, a generalized algorithm to scan or survey a particular field for a swarm of UAVs is proposed. The UAVs will perform their tasks in a self-organized manner autonomously. The proposed algorithm is implemented in python and simulated using webots. The algorithm is tested for various scenarios and it is proven to effectively survey the given field. © 2023 IEEE.

关键词： Antennas

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Analysis of Agricultural Toolset based on Artificial Intelligence 11

Analysis of Agricultural Toolset based on Artificial Intelli...

引用

11th International Conference on System Modeling and Advancement in Research Trends, SMART 2022

作者： Bajpai, Yashi Srivastva, Madhavi Singh, Trilok Chauhan, Vineet Kumar Upadhyay, Diwakar Dixit, Abhishek Invertis University Dept. of Computer Science and Engg. Bareilly India Ritm Dept. of Computer Science and Engg. Lucknow India Tmu Dept. of Computer Science and Engg. Moradabad India Vardhaman College of Engineering Dept. of Information Technology Hyderabad India

ISBN: (纸本)9781665487320

One of the industries that are most crucial to humanity is agriculture. Agriculture mechanization is the major issue facing all countries today. As the world's population is expanding at an incredibly fast rate, there is an increasing demand for food. To fulfill the expanding demand, farmers will need to apply chemical pesticides more often than they already do. The soil is harmed by this. The land continues to be unproductive and barren as a result of this having a substantial influence on agricultural activities. Several mechanization strategies, including deep learning, machine learning, and artificial intelligence, are covered in this article. It is crucial to use new technologies at various stages of the agro-based supply chain due to several long-term challenges for the agricultural industry and various factors, such as population growth, global warming, technological advancement, and the condition of environmental assets (water, etc.). Examples include automated farm equipment processes, the use of sensing devices and satellite data for distant locations, artificial intelligence, and machine learning for forecasting weather patterns. Crop diseases, inadequate storage management, chemical usage, weed control, insufficient irrigation, and poor water management are just a few problems the agricultural sector is facing. Using the range of strategies covered, each of these problems might be handled. It has been demonstrated that automating farming procedures increases soil productivity and improves soil fertility. To get a quick overview of how automation is currently being used in agriculture, this paper examines the work of numerous researchers. In the current study, we highlight the key uses of AI and Ml techniques in farming and highlight the undeniably rising trend in the implementation of these techniques to advance the agriculture sector. © 2022 IEEE.

关键词： Farms

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Decoding Emotions: Facial Expression Recognition and Semantic Rating Prediction with Deep Learning

Decoding Emotions: Facial Expression Recognition and Semanti...

引用

Devices for Integrated Circuit (DevIC)

作者： Soumya Suvra Khan Diganta Sengupta Anupam Ghosh Atal Chaudhuri Dept. of Computer Science and Engineering Meghnad Saha Institute of Technology Kolkata India Dept. of Computer Science and Engineering Heritage Institute of Technology Kolkata India Department of CSE and Department of AIML Netaji Subhash Engineering College Kolkata India Dept. of Computer Science and Engineering Sister Nivedita University Kolkata India

ISBN: (数字)9798350391107

ISBN: (纸本)9798350391114

This work comprises of three studies. We use the Japanese Female Facial Expression (JAFFE) dataset, comprising of 213 images of 10 Japanese female subjects, depicting six primary emotions: Happy, Sad, Surprised, Angry, Disgust, and Fear, to conduct three different studies. In the first study, we developed a Convolutional Neural Network (CNN) model to classify major human facial expressions using only the image data. To facilitate this, we constructed a dataset in CSV format containing two columns: the image file names and t heir corresponding expressions, which were encoded based on the naming convention of the dataset. Our model achieved a testing accuracy of 97.08%, demonstrating its effectiveness in recognizing facial expressions. In our second and third studies, we focused on predicting individual ratings for the expressions using the semantic ratings provided in the accompanying text file. These ratings were derived from psychological evaluations on a 5-point scale for the six emotions. We developed a multi-class regression CNN model to quantify these ratings. The second study included all six expressions and yielded a testing accuracy of 75.82%, while the third study, which excluded the expression of Fear, achieved a higher testing accuracy of 86.06%. These findings suggest that Fear may be processed differently from the other basic facial expressions, consistent with observations in psychological studies. This work highlights the potential of using deep learning techniques to quantify and understand the subtle distinctions of human facial expressions.

关键词： Deep learning Performance evaluation Emotion recognition Accuracy Face recognition Semantics Psychology Predictive models Convolutional neural networks Testing

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Disinformation Classification Using Transformer based Machine Learning 6

Disinformation Classification Using Transformer based Machin...

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6th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2023

作者： Al-Alshaqi, Mohammed Rawat, Danda B. Howard University Dept. of Electrical Engineering and Computer Science WashingtonDC United States Howard University WashingtonDC United States

ISBN: (纸本)9798350358346

The proliferation of false information via social media has become an increasingly pressing problem in recent years when it comes to identifying authentic facts. Social media is known as the primary source of influencing people. The identification of fake news has been a topic of interest in academia, aiming to report on the questionable authenticity behind shared facts over the internet. The term 'fake news' refers to unauthentic news that presents baseless facts to create propaganda and influence the perception of the audience. Consequently, an automatic approach to detecting fake news has been suggested as a viable solution to curbing the spread of disinformation. However, developing an automated solution for fake news detection raises concerns due to the high risk of inaccurately classifying data. Therefore, improving classification schemes has been recognized as a relevant solution for accurately interpreting the data. In this regard, our research work focuses on developing a transformer-based fake news classification model. Many machine learning models have been reported to lack a word embedding process. Thus, we employ transformers to extract word embeddings from news articles. The proposed scheme is compared with the baseline Hybrid CNN and RNN techniques using matrix indicators such as the F1-score, precision, recall, and accuracy. Based on the results obtained from our presented classifier model, the proposed methodology outperforms earlier approaches, achieving significantly enhanced accuracy in the detection of fake news. © 2023 IEEE.

关键词： Fake detection

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A Video Feature Information Extraction and Recognition Method in Metaverse Space

A Video Feature Information Extraction and Recognition Metho...

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2023 International Conference on Artificial Intelligence and Power engineering, AIPE 2023

作者： Park, Byeongchan Yoo, Injae Jang, Seyoung Lee, Jaechung Kim, Seok-Yoon Kim, Youngmo Soongil University Dept. Computer Science and Engineering Seoul Korea Republic of Institute of Technology BeyondTech Inc. Seoul Korea Republic of

ISBN: (纸本)9798350310788

Contents provided within the 3D space of the Metaverse can be illegally copied, and may lead to unintentional copyright infringement by users. Filtering technology, which is an existing technology for identifying illegal copyrighted works of video, is focused on discriminating 2D video, so it has a limitation that it is difficult to use it as a base technology for copyright protection of 3D video such as VR. In this paper, as a method for extracting and recognizing videos provided in the metaverse space such as 3D videos, we propose a method for extracting and recognizing video feature information in the metaverse space that determines whether the video is the same by comparing the original video and the query video. As a result of the experiment of the proposed method, the excellent recognition rate has been confirmed with a recognition rate of 98% and an unrecognition rate of 2%. © 2023 IEEE.

关键词： Copyrights

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Optimizing the Response Time for ROS Tasks in Multi-Core Processors 27

Optimizing the Response Time for ROS Tasks in Multi-Core Pro...

引用

27th IEEE/ACM International Symposium on Distributed Simulation and Real Time Applications, DS-RT 2023

作者： Kim, Sunho Park, Hayeon Lee, Chang-Gun Seoul National University Dept. of Mechanical Engineering Seoul Korea Republic of Seoul National University Dept. of Computer Science Seoul Korea Republic of

ISBN: (纸本)9798350337846

This paper presents methods to optimize the response time of ROS (Robot Operating System), a widely utilized open-source meta-operating system in robotic software development. Despite its popularity, ROS lacks real-time capabilities, making it unsuitable for real-time control and difficult to use in embedded systems. Recently, DAG (Directed Acyclic Graph) task scheduling algorithms have gained much attention, but they are challenging to apply in the current design of ROS. In this work, we analyze that there are three major challenges in ROS;(i) misalignment delay, (ii) message delivery mechanism using TCP/IP, and (iii) multiple overlapping instances of a single task. We first calculate the new response time bound considering misalignment delay and propose an optimization technique using it to solve the first problem. Furthermore, we address the remaining challenges by assigning priority to the nodes of the DAG and ksoftirqd processes. Our experiments using random tasks show significant improvement, outperforming the state-of-art methods. In addition, our methods are validated through testing autonomous driving software on embedded systems, proving their real-world applicability. © 2023 IEEE.

关键词： Multitasking

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Security using Flexible Decisions for Trust Value in a Ubiquitous Environment 10

Security using Flexible Decisions for Trust Value in a Ubiqu...

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Goyal, Sonali Batra, Neera Kaur, Amandeep Sharma, Vikrant University Department of Cse Haryana Mullana India Graphic Era Hill University Dept. of Computer Science and Engineering Uttarakhand Dehradun India

ISBN: (纸本)9789380544472

Ubiquitous computing refers to the ability to access information and software programs from any location and at any time, utilizing computing and communication capabilities that are no longer limited to human consumers. Unlike desktop computing, ubiquitous computing can be done anywhere, on any device, and in any format. This article explains how currently available solutions are adequate for managing service processing, and a distributed access design is used to achieve this goal. Two topics are addressed in this article: (1) To use of a controlled architecture to execute user authorization (2) The calculation of trust values, which will improve user protection. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Ubiquitous computing

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Intelligent System for Detection of Intrusion in Imbalanced Network Traffic

Intelligent System for Detection of Intrusion in Imbalanced ...

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International Conference on Confluence The Next Generation Information Technology Summit (Confluence)

作者： Piyush Bagla Amit Kumar Mishra Neeraj Kumar Pandey Ravi Sharma Ankur Dumka Department of Computer Science & Engineering National Institute of Technology Jalandhar India Dept. of Computer Science & Engineering Graphic Era Hill University Dehradun (India) Department of Computer Science & Engineering Graphic Era (Deemed to be University) Dehradun India Department of Computer Science & Engineering Women Institute of Technology Dehradun Uttarakhand (India)

Malicious cyberattacks can frequently hide among enormous amounts of regular data in networks with uneven traffic patterns. The identification of imbalance network traffic is difficult to find and making a challenge in terms of Signature building. Despite years of improvement, IDSs still struggle to increase detection accuracy. There are distinct machine learning or deep learning algorithms provides the better results and accuracy for imbalance network traffic. In this study, intrusion detection in unbalanced network traffic is accomplished using both machine learning and deep learning. This process a DSSTE algorithm to avoid imbalance problems. Initially the training set is pre-processed to modify imbalanced data and features are extracted. The proposed model is evaluated with trained data using multiple classification algorithms.

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Energy Efficient Coverage Optimization in Mobile Wireless Sensor Network Using Grey Wolf Algorithm 14

Energy Efficient Coverage Optimization in Mobile Wireless Se...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Gupta, Ankit Mamatha, K.M. Kiran, M. National Institute of Technology Karnataka Dept. of Information Technology Surathkal India Viseveshvaraya Technological University Dept. of Computer Science and Engineering Belgaum India

ISBN: (纸本)9798350335095

The issue of decreased coverage rate in Mobile Wireless Sensor Networks (MWSNs), caused by mobile sensor nodes being randomly placed inside a monitoring area. Additionally, it becomes extremely important to utilise a sensor node's energy very effectively due to the finite energy of sensor nodes. Hence, to provide optimised positions for the sensor nodes while using the energy of sensor nodes adeptly authors propose an energy efficient coverage algorithm. Initially, article focus on optimal placement of the sensor nodes within a area to achieve the maximum coverage and later authors have focused on improvising the network lifetime. Article presents a combination of Grey Wolf Optimization and Virtual Force algorithm for optimization of coverage in MWSN. Further, to improve the network lifetime, a GWO-based clustering algorithm is presented using distance and energy as a parameter. The algorithms are implemented and simulated on Matlab. The efficiency of the presented algorithm is observed comparing with other Swarm Intelligence (SI) based optimization algorithms, like GWO, VFA, Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Ant-lion Optimization (ALO) and the results of the GWO-based clustering is compared with the traditional LEACH algorithm and energy-balanced clustering based on PSO. Simulation results demonstrate that the presented algorithms outperform the considered algorithms. © 2023 IEEE.

关键词： Sensor nodes

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